agenta-connection-issues
Sub-skill of agenta: Connection Issues (+2).
Best use case
agenta-connection-issues is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of agenta: Connection Issues (+2).
Teams using agenta-connection-issues should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/connection-issues/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agenta-connection-issues Compares
| Feature / Agent | agenta-connection-issues | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of agenta: Connection Issues (+2).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Connection Issues (+2)
## Connection Issues
```python
# Problem: Cannot connect to Agenta host
# Solution: Verify host and network settings
def diagnose_connection(host: str):
import requests
try:
response = requests.get(f"{host}/api/health", timeout=5)
if response.status_code == 200:
print("Connection successful")
else:
print(f"Server returned: {response.status_code}")
except requests.exceptions.ConnectionError:
print("Cannot reach server - check host/port")
except requests.exceptions.Timeout:
print("Connection timed out - server may be overloaded")
```
## Evaluation Failures
```python
# Problem: Evaluations failing or inconsistent
# Solution: Add retry logic and validation
def robust_evaluation(prompt: str, max_retries: int = 3):
for attempt in range(max_retries):
try:
result = ag.llm.complete(prompt=prompt)
if validate_result(result):
return result
except Exception as e:
if attempt == max_retries - 1:
raise
time.sleep(2 ** attempt)
```
## Version Conflicts
```python
# Problem: Multiple team members editing same variant
# Solution: Use branching strategy
def create_branch_variant(base_variant: str, branch_name: str):
# Clone variant for isolated development
base = client.get_variant_by_name(app_name, base_variant)
return client.create_variant(
app_name=app_name,
variant_name=f"{base_variant}-{branch_name}",
config=base.config
)
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